Build AI agents that
don't fail in production.
Observe, evaluate, debug, and continuously improve AI agents before your users notice problems. Real-time distributed tracing, guardrails, and hallucination scoring in one unified platform.
Agent Execution Health
NominalReal-time Execution Traces Stream
Auto-refreshing| Trace ID | Agent | Step / Tool | Status | Latency | Tokens | Hallucination |
|---|---|---|---|---|---|---|
| tr_89a3f | CustomerOps-v3 | Execute SQL Tool | OK | 42ms | 412 | 0.01 |
| tr_89a3e | CustomerOps-v3 | Vector Search Context | OK | 18ms | 890 | 0 |
| tr_89a3d | RefundApprover | Verify User Balance | VERIFY | 184ms | 1250 | 0.12 |
| tr_89a3c | CodeAssistant | Synthesize Response | OK | 320ms | 2310 | 0.03 |
Engineered for Modern Agent Frameworks & Production Stacks
SOC 2 Type II Ready
Enterprise-grade security controls & continuous compliance auditing
Zero Data Retention (ZDR)
Telemetry payloads are processed in-memory without persistent prompt storage
Self-Hosted VPC Option
Deploy AgentReliability via Docker / Kubernetes inside your own cloud boundary
OpenTelemetry Native
Standardized OTLP traces exportable to Datadog, Honeycomb, or Jaeger
Why AI agents fail in production
Standard application performance monitoring (APM) tools cannot detect non-deterministic agent failures. AgentReliability catches and repairs failures at the reasoning layer.
{
"error": "TypeError: Cannot read properties of undefined (reading 'customer_id')",
"tool_name": "stripe_create_charge",
"input_args": { "amount": "$49.00", "currency": "USD" },
"cause": "Passed string '$49.00' instead of integer 4900 cents schema expectation"
}{
"status": "INTERCEPTED_BY_GUARDRAIL",
"action": "AUTO_REPAIR_SCHEMA",
"original_args": { "amount": "$49.00" },
"repaired_args": { "amount": 4900, "currency": "usd" },
"retry_result": "200_OK",
"latency_penalty": "2ms"
}How AgentReliability works
Four simple steps to transform unpredictable AI prompts into deterministic, production-grade infrastructure.
Auto-instrument agents with 2 lines of SDK code
Capture every LLM call, vector search, tool execution, and state mutation with near-zero latency overhead (< 1.2ms).
from agent_reliability import AgentMonitor
# Initialize AgentReliability monitoring wrapper
monitor = AgentMonitor(api_key="ar_live_...", env="production")
@monitor.trace_agent(name="CustomerSupportAgent")
def run_agent(user_query: str):
response = agent.execute(user_query)
return responseFull-stack distributed tracing for AI agents
Never guess why an agent got stuck. Visualize every parent span, tool invocation, token count, and memory mutation in a waterfall view built for engineers.
Span Payload: LLM Reasoning: GPT-4o Task Decomposition
Provider: OpenAI gpt-4o (2024-08){
"system_prompt": "You are a customer support agent. Validate invoice status via postgres and execute refund via stripe if duplicate charge confirmed.",
"user_prompt": "I was charged twice for invoice #INV-9402 on August 1st. Please refund $49.00."
}Thought: First check postgres database for invoice #INV-9402 to confirm duplicate status. Then call stripe_refund.
Span Performance240 ms
Built for mission-critical AI applications
Everything you need to monitor, evaluate, and scale autonomous AI agents in enterprise production environments.
Distributed Agent Tracing
Complete visibility into multi-agent sub-tasks and tool chains.
Trace non-deterministic LLM execution paths, vector queries, memory state mutations, and external API requests with OpenTelemetry standards.
Agent Reliability Score
A single 0-100 metric for production readiness.
Continuously aggregate tool success rates, hallucination indices, and latency variance into a unified SLA score.
Real-Time Evaluations
Automated factuality, grounding & safety assertions.
Run continuous online evaluations on live completions without slowing down inference response times.
Inline Guardrails & Auto-Repair
Intercept and correct bad tool args before execution.
Automatically sanitize malformed JSON, enforce strict type schemas, and redact PII credentials in real-time.
Production Alerts & Error Budgets
Instant alerts on P99 spikes, loops & hallucination clusters.
Configure custom PagerDuty and Slack webhooks when hallucination rates breach your engineering SLA threshold.
Monitoring every layer of the agent stack
From the initial user request down to vector queries and tool execution, AgentReliability wraps your infrastructure with zero-latency guardrails.
State Mutation & Infinite Loop Interception
Tracks agent task decomposition, memory window sliding context, sub-goal planning, and recursive loop detection.
Asynchronous background batching ensures zero impact on end-user response time.
Zero-downtime architecture with automatic circuit breakers and failover routing.
Scales seamlessly from early prototypes to high-throughput enterprise clusters.
Catch hallucinations, malformed tool arguments, and infinite loops before users do.
Frequently Asked Questions
Everything you need to know about integrating AgentReliability into your AI stack.
Stop guessing why your AI agents break.
Join 4,000+ engineers building production-grade, reliable AI infrastructure with AgentReliability.